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IDN-tensorflow

[Original Caffe version]

Testing

  • Install Tensorflow 1.11, Matlab R2017a
  • Download Test datasets
  • Modify config.py (if you want to test x3 model on Set14, config.TEST.model_path = 'checkpoint_x3/model.ckpt' config.TEST.dataset = 'Set14') and test.py (scale = 3).
  • Run testing:
python test.py

Training

  • Download Training dataset
  • Modify config.py (if you want to train x4 model, config.TRAIN.hr_img_path = '/path/to/DIV2K_train_HR/' config.TRAIN.checkpoint_dir = 'checkpoint_x4/' config.VALID.hr_img_path = '/path/to/DIV2K_valid_HR/' config.VALID.lr_img_path = '/path/to/DIV2K_valid_LR_x4/') and train_SR.py (scale = 4)
  • Run training:
python train_SR.py

Note

This TensorFlow version is trained with DIV2K training dataset on RGB channels. Additionally, We modify the upsample layer to subpixel convolution (the original version is transposed convolution).

Results

Test_results

The following PSNR/SSIMs are evaluated on Matlab R2017a and the code can be referred to Evaluate_PSNR_SSIM.m.

Training dataset Scale Set5 Set14 B100 Urban100
291 ×2 37.83 / 0.9600 33.30 / 0.9148 32.08 / 0.8985 31.27 / 0.9196
DIV2K ×2 37.85 / 0.9598 33.58 / 0.9178 32.11 / 0.8989 31.95 / 0.9266
291 ×3 34.11 / 0.9253 29.99 / 0.8354 28.95 / 0.8013 27.42 / 0.8359
DIV2K ×3 34.24 / 0.9260 30.27 / 0.8408 29.03 / 0.8038 27.99 / 0.8489
291 ×4 31.82 / 0.8903 28.25 / 0.7730 27.41 / 0.7297 25.41 / 0.7632
DIV2K ×4 31.99 / 0.8928 28.52 / 0.7794 27.52 / 0.7339 25.92 / 0.7801

Model Parameters

Scale Model size
×2 579,276
×3 587,931
×4 600,048

Citation

If you find IDN useful in your research, please consider citing:

@inproceedings{Hui-IDN-2018,
  title={Fast and Accurate Single Image Super-Resolution via Information Distillation Network},
  author={Hui, Zheng and Wang, Xiumei and Gao, Xinbo},
  booktitle={CVPR},
  pages = {723--731},
  year={2018}
}

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Tensorflow implementation of IDN (CVPR 2018)

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